Group-oriented project recommendation method based on joint probability matrix decomposition

Abstract

The invention discloses a group-oriented project recommendation method based on joint probability matrix decomposition. The group-oriented project recommendation method is characterized by comprising the following steps: (1), representing project scoring information of a user and information that the user belongs to a group by using a triple; (2), calculating the user relevance based on the triple of the information that the user belongs to the group; (3), implementing a joint probability matrix decomposition method based on the user relevance so as to obtain a user characteristic matrix and a project characteristic matrix; (4), calculating by utilizing an average strategy so as to obtain a group characteristic matrix; and (5), obtaining previous N projects having the highest project predication scoring in each group so as to obtain a recommendation list. According to the invention, the user relevance based on group information is integrated in probability matrix decomposition; joint probability matrix decomposition based on the user relevance is implemented; furthermore, the group characteristic matrix is obtained by calculation through the average strategy; therefore, a group-oriented recommendation result is obtained; and more accurate individual services can be provided for the group to a certain degree.

Description

technical field [0001] The invention relates to the technical field of computer applications, in particular to a group-oriented item recommendation method based on joint probability matrix decomposition. Background technique [0002] With the rapid development of information technology and social networks, various virtual communities are constantly emerging, and the communication between users in the communities is becoming more and more convenient. Frequent community activities make users form groups and participate in some activities together, and their behavior shows certain group characteristics. Therefore, more and more users of recommendation systems are shifting from individuals to groups. Group recommendation is to recommend information and items of interest to users in the group based on their comprehensive preferences. At present, group recommendation systems such as MusixFX, PolyLens, and TV4M have been developed and widely used, which greatly reduce the time and...

AI Technical Summary

Problems solved by technology

However, the existing group-oriented recommendation methods do not consider the important factor of the correlation among members in the group, which reduces the accuracy of the group-oriented recommendation system.

[0005] (2) Synthesis stage problem of group recommendation

However, after the user feature matrix is ​​obtained by performing matrix decomposition on the user-item rating matrix, the feature vectors of the users in the group are synthesized using a synthesis strategy to obtain the feature vector of the group. The synthesis problem at this stage has not been studied yet.

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